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Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss

Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer representations.

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Cite this as

Jaidev Gill, Vala Vakilian, Christos Thrampoulidis (2024). Dataset: Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss. https://doi.org/10.57702/arr5v9yv

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Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Defined In	https://doi.org/10.48550/arXiv.2310.00893
Author	Jaidev Gill
More Authors	Vala Vakilian Christos Thrampoulidis
Homepage	https://arxiv.org/abs/2303.12345